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Articulatory-to-acoustic (forward) mapping is a technique to predict speechusing various articulatory acquisition techniques (e.g. ultrasound tongueimaging, lip video). Real-time MRI (rtMRI) of the vocal tract has not been usedbefore for this purpose. The advantage of MRI is that it has a high `relative'spatial resolution: it can capture not only lingual, labial and jaw motion, butalso the velum and the pharyngeal region, which is typically not possible withother techniques. In the current paper, we train various DNNs (fully connected,convolutional and recurrent neural networks) for articulatory-to-speechconversion, using rtMRI as input, in a speaker-specific way. We use two maleand two female speakers of the USC-TIMIT articulatory database, each of themuttering 460 sentences. We evaluate the results with objective (Normalized MSEand MCD) and subjective measures (perceptual test) and show that CNN-LSTMnetworks are preferred which take multiple images as input, and achieve MCDscores between 2.8-4.5 dB. In the experiments, we find that the predictions ofspeaker `m1' are significantly weaker than other speakers. We show that this iscaused by the fact that 74% of the recordings of speaker `m1' are out of sync.

Speaker dependent articulatory-to-acoustic mapping using real-time MRI of the vocal tract